Image Forgery Localization via Guided Noise and Multi-Scale Feature Aggregation

TL;DR

The paper presents elsarticle.cls, a LaTeX document class redesigned for Elsevier submissions to improve compatibility and formatting reliability. Built on article.cls, it minimizes clashes with common packages while supporting standard tools like natbib and hyperref, and it provides preprint and final model formatting options ($1+$, $3+$, $5+$). It details major differences from the older elsart.cls and outlines straightforward installation via Elsevier resources or CTAN, including generation of the cls file from dtx/ins sources. The result is a robust, flexible class that streamlines manuscript preparation for Elsevier journals and accommodates a range of layout and front matter needs.

Abstract

Image Forgery Localization (IFL) technology aims to detect and locate the forged areas in an image, which is very important in the field of digital forensics. However, existing IFL methods suffer from feature degradation during training using multi-layer convolutions or the self-attention mechanism, and perform poorly in detecting small forged regions and in robustness against post-processing. To tackle these, we propose a guided and multi-scale feature aggregated network for IFL. Spectifically, in order to comprehensively learn the noise feature under different types of forgery, we develop an effective noise extraction module in a guided way. Then, we design a Feature Aggregation Module (FAM) that uses dynamic convolution to adaptively aggregate RGB and noise features over multiple scales. Moreover, we propose an Atrous Residual Pyramid Module (ARPM) to enhance features representation and capture both global and local features using different receptive fields to improve the accuracy and robustness of forgery localization. Expensive experiments on 5 public datasets have shown that our proposed model outperforms several the state-of-the-art methods, specially on small region forged image.